Overview

Dataset statistics

Number of variables17
Number of observations488244
Missing cells5319
Missing cells (%)0.1%
Duplicate rows524
Duplicate rows (%)0.1%
Total size in memory63.3 MiB
Average record size in memory136.0 B

Variable types

NUM9
CAT8

Warnings

Dataset has 524 (0.1%) duplicate rows Duplicates
Hotel_Address has a high cardinality: 1422 distinct values High cardinality
Review_Date has a high cardinality: 731 distinct values High cardinality
Hotel_Name has a high cardinality: 1421 distinct values High cardinality
Reviewer_Nationality has a high cardinality: 226 distinct values High cardinality
Negative_Review has a high cardinality: 311852 distinct values High cardinality
Positive_Review has a high cardinality: 391112 distinct values High cardinality
Tags has a high cardinality: 53140 distinct values High cardinality
days_since_review has a high cardinality: 731 distinct values High cardinality
Review_Total_Negative_Word_Counts has 121589 (24.9%) zeros Zeros
Review_Total_Positive_Word_Counts has 33837 (6.9%) zeros Zeros

Reproduction

Analysis started2020-12-09 14:55:01.772815
Analysis finished2020-12-09 14:55:47.231073
Duration45.46 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Hotel_Address
Categorical

HIGH CARDINALITY

Distinct1422
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
163 Marsh Wall Docklands Tower Hamlets London E14 9SJ United Kingdom
 
4789
372 Strand Westminster Borough London WC2R 0JJ United Kingdom
 
4256
Scarsdale Place Kensington Kensington and Chelsea London W8 5SY United Kingdom
 
3578
7 Pepys Street City of London London EC3N 4AF United Kingdom
 
3212
1 Inverness Terrace Westminster Borough London W2 3JP United Kingdom
 
2958
Other values (1417)
469451 
ValueCountFrequency (%) 
163 Marsh Wall Docklands Tower Hamlets London E14 9SJ United Kingdom47891.0%
 
372 Strand Westminster Borough London WC2R 0JJ United Kingdom42560.9%
 
Scarsdale Place Kensington Kensington and Chelsea London W8 5SY United Kingdom35780.7%
 
7 Pepys Street City of London London EC3N 4AF United Kingdom32120.7%
 
1 Inverness Terrace Westminster Borough London W2 3JP United Kingdom29580.6%
 
225 Edgware Road Westminster Borough London W2 1JU United Kingdom26280.5%
 
4 18 Harrington Gardens Kensington and Chelsea London SW7 4LH United Kingdom25650.5%
 
1 Waterview Drive Greenwich London SE10 0TW United Kingdom25510.5%
 
27 Devonshire Terrace Westminster Borough London W2 3DP United Kingdom22880.5%
 
Lakeside Way Brent London HA9 0BU United Kingdom22270.5%
 
Other values (1412)45719293.6%
 
2020-12-09T14:55:47.363065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:55:47.621794image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length96
Median length60
Mean length60.17488592
Min length34

Additional_Number_of_Scoring
Real number (ℝ≥0)

Distinct471
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.438969
Minimum1
Maximum2682
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:47.867549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile57
Q1168
median338
Q3639
95-th percentile1427
Maximum2682
Range2681
Interquartile range (IQR)471

Descriptive statistics

Standard deviation467.9015792
Coefficient of variation (CV)0.9718813999
Kurtosis5.734583708
Mean481.438969
Median Absolute Deviation (MAD)200
Skewness2.149221449
Sum235059688
Variance218931.8878
MonotocityNot monotonic
2020-12-09T14:55:48.108629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
268247891.0%
 
228842560.9%
 
183135780.7%
 
193632120.7%
 
25630790.6%
 
127429580.6%
 
83229340.6%
 
21128580.6%
 
148526280.5%
 
70426000.5%
 
Other values (461)45535293.3%
 
ValueCountFrequency (%) 
113< 0.1%
 
412< 0.1%
 
539< 0.1%
 
6118< 0.1%
 
756< 0.1%
 
ValueCountFrequency (%) 
268247891.0%
 
228842560.9%
 
193632120.7%
 
183135780.7%
 
148526280.5%
 

Review_Date
Categorical

HIGH CARDINALITY

Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
8/2/2017
 
2453
9/15/2016
 
2183
4/5/2017
 
2169
8/30/2016
 
1860
2/16/2016
 
1855
Other values (726)
477724 
ValueCountFrequency (%) 
8/2/201724530.5%
 
9/15/201621830.4%
 
4/5/201721690.4%
 
8/30/201618600.4%
 
2/16/201618550.4%
 
7/5/201618070.4%
 
5/31/201617760.4%
 
7/12/201617240.4%
 
12/5/201616990.3%
 
8/2/201616930.3%
 
Other values (721)46902596.1%
 
2020-12-09T14:55:48.380875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:55:48.614587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length9
Mean length8.931763626
Min length8

Average_Score
Real number (ℝ≥0)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.401896593
Minimum6.4
Maximum9.8
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:48.859440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile7.4
Q18.1
median8.4
Q38.8
95-th percentile9.2
Maximum9.8
Range3.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5508706658
Coefficient of variation (CV)0.06556503758
Kurtosis0.282624906
Mean8.401896593
Median Absolute Deviation (MAD)0.4
Skewness-0.5291766522
Sum4102175.6
Variance0.3034584905
MonotocityNot monotonic
2020-12-09T14:55:49.063176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
8.4384957.9%
 
8.5371597.6%
 
8.6359947.4%
 
8.1340407.0%
 
8.2338696.9%
 
8.7319166.5%
 
8.3304816.2%
 
8.8299326.1%
 
8.9277835.7%
 
9.1211234.3%
 
Other values (23)16745234.3%
 
ValueCountFrequency (%) 
6.411630.2%
 
6.64000.1%
 
6.79650.2%
 
6.813350.3%
 
6.917370.4%
 
ValueCountFrequency (%) 
9.828< 0.1%
 
9.69150.2%
 
9.512070.2%
 
9.491261.9%
 
9.3124162.5%
 

Hotel_Name
Categorical

HIGH CARDINALITY

Distinct1421
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
Britannia International Hotel Canary Wharf
 
4789
Strand Palace Hotel
 
4256
Copthorne Tara Hotel London Kensington
 
3578
DoubleTree by Hilton Hotel London Tower of London
 
3212
Grand Royale London Hyde Park
 
2958
Other values (1416)
469451 
ValueCountFrequency (%) 
Britannia International Hotel Canary Wharf47891.0%
 
Strand Palace Hotel42560.9%
 
Copthorne Tara Hotel London Kensington35780.7%
 
DoubleTree by Hilton Hotel London Tower of London32120.7%
 
Grand Royale London Hyde Park29580.6%
 
Hilton London Metropole26280.5%
 
Millennium Gloucester Hotel London25650.5%
 
Intercontinental London The O225510.5%
 
Park Grand Paddington Court22880.5%
 
Hilton London Wembley22270.5%
 
Other values (1411)45719293.6%
 
2020-12-09T14:55:49.312544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:55:49.588285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length24
Mean length25.31215745
Min length2

Reviewer_Nationality
Categorical

HIGH CARDINALITY

Distinct226
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
United Kingdom
235230 
United States of America
33731 
Australia
 
20569
Ireland
 
14032
United Arab Emirates
 
9666
Other values (221)
175016 
ValueCountFrequency (%) 
United Kingdom 23523048.2%
 
United States of America 337316.9%
 
Australia 205694.2%
 
Ireland 140322.9%
 
United Arab Emirates 96662.0%
 
Saudi Arabia 84941.7%
 
Netherlands 82261.7%
 
Switzerland 79381.6%
 
Canada 75151.5%
 
Germany 73121.5%
 
Other values (216)13553127.8%
 
2020-12-09T14:55:49.876929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)< 0.1%
2020-12-09T14:55:50.119007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length16
Mean length14.08348285
Min length1

Negative_Review
Categorical

HIGH CARDINALITY

Distinct311852
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
No Negative
121589 
Nothing
 
13706
Nothing
 
4029
nothing
 
2106
N A
 
981
Other values (311847)
345833 
ValueCountFrequency (%) 
No Negative12158924.9%
 
Nothing137062.8%
 
Nothing 40290.8%
 
nothing21060.4%
 
N A9810.2%
 
None9350.2%
 
7820.2%
 
N a4910.1%
 
Breakfast3680.1%
 
Small room3550.1%
 
Other values (311842)34290270.2%
 
2020-12-09T14:55:51.589310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique305692 ?
Unique (%)62.6%
2020-12-09T14:55:51.848472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1966
Median length41
Mean length93.31080566
Min length1

Review_Total_Negative_Word_Counts
Real number (ℝ≥0)

ZEROS

Distinct401
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.44131418
Minimum0
Maximum408
Zeros121589
Zeros (%)24.9%
Memory size3.7 MiB
2020-12-09T14:55:52.067608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q323
95-th percentile68
Maximum408
Range408
Interquartile range (IQR)21

Descriptive statistics

Standard deviation29.57228685
Coefficient of variation (CV)1.603588907
Kurtosis31.63902257
Mean18.44131418
Median Absolute Deviation (MAD)9
Skewness4.420730067
Sum9003861
Variance874.5201498
MonotocityNot monotonic
2020-12-09T14:55:52.267434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
012158924.9%
 
2234384.8%
 
3172653.5%
 
6168463.5%
 
5158873.3%
 
7152633.1%
 
4142972.9%
 
8139812.9%
 
9129422.7%
 
10117512.4%
 
Other values (391)22498546.1%
 
ValueCountFrequency (%) 
012158924.9%
 
2234384.8%
 
3172653.5%
 
4142972.9%
 
5158873.3%
 
ValueCountFrequency (%) 
4081< 0.1%
 
4032< 0.1%
 
4021< 0.1%
 
4011< 0.1%
 
3992< 0.1%
 

Total_Number_of_Reviews
Real number (ℝ≥0)

Distinct1095
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2573.642566
Minimum43
Maximum9568
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:52.487621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile423
Q11140
median2058
Q33550
95-th percentile6977
Maximum9568
Range9525
Interquartile range (IQR)2410

Descriptive statistics

Standard deviation1947.060953
Coefficient of variation (CV)0.756538992
Kurtosis1.805900783
Mean2573.642566
Median Absolute Deviation (MAD)1059
Skewness1.37410498
Sum1256565541
Variance3791046.353
MonotocityNot monotonic
2020-12-09T14:55:52.721211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
908647891.0%
 
956842560.9%
 
710535780.7%
 
749132120.7%
 
653929580.6%
 
697726280.5%
 
572625650.5%
 
420425510.5%
 
660822880.5%
 
430522270.5%
 
Other values (1085)45719293.6%
 
ValueCountFrequency (%) 
4312< 0.1%
 
4512< 0.1%
 
4940< 0.1%
 
5113< 0.1%
 
5413< 0.1%
 
ValueCountFrequency (%) 
956842560.9%
 
908647891.0%
 
817718090.4%
 
758616860.3%
 
749132120.7%
 

Positive_Review
Categorical

HIGH CARDINALITY

Distinct391112
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
No Positive
 
33837
Location
 
8773
Everything
 
2160
location
 
1594
Nothing
 
1171
Other values (391107)
440709 
ValueCountFrequency (%) 
No Positive338376.9%
 
Location87731.8%
 
Everything21600.4%
 
location15940.3%
 
Nothing11710.2%
 
The location10910.2%
 
Great location9970.2%
 
Good location8910.2%
 
Location 8740.2%
 
Everything 5770.1%
 
Other values (391102)43627989.4%
 
2020-12-09T14:55:54.728399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique382141 ?
Unique (%)78.3%
2020-12-09T14:55:55.009215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1960
Median length59
Mean length94.73031927
Min length1

Review_Total_Positive_Word_Counts
Real number (ℝ≥0)

ZEROS

Distinct364
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean17.79280399
Minimum0
Maximum395
Zeros33837
Zeros (%)6.9%
Memory size3.7 MiB
2020-12-09T14:55:55.276626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q322
95-th percentile56
Maximum395
Range395
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.8136532
Coefficient of variation (CV)1.225981763
Kurtosis32.88995015
Mean17.79280399
Median Absolute Deviation (MAD)7
Skewness4.188480032
Sum8687212
Variance475.8354661
MonotocityNot monotonic
2020-12-09T14:55:55.530297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0338376.9%
 
6254715.2%
 
5253805.2%
 
4233134.8%
 
7232494.8%
 
8220004.5%
 
3213644.4%
 
9200214.1%
 
2198034.1%
 
10186323.8%
 
Other values (354)25517352.3%
 
ValueCountFrequency (%) 
0338376.9%
 
2198034.1%
 
3213644.4%
 
4233134.8%
 
5253805.2%
 
ValueCountFrequency (%) 
3951< 0.1%
 
3861< 0.1%
 
3842< 0.1%
 
3832< 0.1%
 
3821< 0.1%
 
Distinct196
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.115956604
Minimum1
Maximum355
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:56.047315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile26
Maximum355
Range354
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.99577179
Coefficient of variation (CV)1.545227494
Kurtosis52.97586383
Mean7.115956604
Median Absolute Deviation (MAD)2
Skewness5.141920738
Sum3474316
Variance120.9069972
MonotocityNot monotonic
2020-12-09T14:55:56.246581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
114732330.2%
 
26377413.1%
 
3445369.1%
 
4331036.8%
 
5261045.3%
 
6213614.4%
 
7175673.6%
 
8152033.1%
 
9127702.6%
 
10110032.3%
 
Other values (186)9549919.6%
 
ValueCountFrequency (%) 
114732330.2%
 
26377413.1%
 
3445369.1%
 
4331036.8%
 
5261045.3%
 
ValueCountFrequency (%) 
3551< 0.1%
 
3301< 0.1%
 
3154< 0.1%
 
2972< 0.1%
 
2812< 0.1%
 

Reviewer_Score
Real number (ℝ≥0)

Distinct37
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8.402732246
Minimum2.5
Maximum10
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:56.452549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile5
Q17.5
median8.8
Q39.6
95-th percentile10
Maximum10
Range7.5
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.635967246
Coefficient of variation (CV)0.1946946777
Kurtosis0.983732752
Mean8.402732246
Median Absolute Deviation (MAD)1.2
Skewness-1.198513722
Sum4102575.2
Variance2.67638883
MonotocityNot monotonic
2020-12-09T14:55:56.634916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
1011068622.7%
 
9.66759713.8%
 
9.25546411.4%
 
8.8437549.0%
 
8.3388027.9%
 
7.5329006.7%
 
7.9311806.4%
 
7.1234004.8%
 
6.7176613.6%
 
6.3140412.9%
 
Other values (27)5275810.8%
 
ValueCountFrequency (%) 
2.520460.4%
 
2.914840.3%
 
334< 0.1%
 
3.18< 0.1%
 
3.326250.5%
 
ValueCountFrequency (%) 
1011068622.7%
 
9.66759713.8%
 
9.56900.1%
 
9.457< 0.1%
 
9.25546411.4%
 

Tags
Categorical

HIGH CARDINALITY

Distinct53140
Distinct (%)10.9%
Missing1
Missing (%)< 0.1%
Memory size3.7 MiB
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
 
4705
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
 
4661
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
 
4220
[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']
 
3959
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']
 
2949
Other values (53135)
467749 
ValueCountFrequency (%) 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']47051.0%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']46611.0%
 
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']42200.9%
 
[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']39590.8%
 
[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']29490.6%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']29340.6%
 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']27820.6%
 
[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ']25750.5%
 
[' Leisure trip ', ' Couple ', ' Standard Double Room ', ' Stayed 1 night ']24780.5%
 
[' Leisure trip ', ' Couple ', ' Deluxe Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']22910.5%
 
Other values (53130)45468993.1%
 
2020-12-09T14:55:57.001478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique28802 ?
Unique (%)5.9%
2020-12-09T14:55:57.231972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length213
Median length108
Mean length102.4303627
Min length3

days_since_review
Categorical

HIGH CARDINALITY

Distinct731
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size3.7 MiB
1 days
 
2453
322 day
 
2183
120 day
 
2169
338 day
 
1860
534 day
 
1855
Other values (726)
477723 
ValueCountFrequency (%) 
1 days24530.5%
 
322 day21830.4%
 
120 day21690.4%
 
338 day18600.4%
 
534 day18550.4%
 
394 day18070.4%
 
429 day17760.4%
 
387 day17240.4%
 
241 day16990.3%
 
366 day16930.3%
 
Other values (721)46902496.1%
 
2020-12-09T14:55:57.448571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T14:55:57.654284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.984126789
Min length3

lat
Real number (ℝ≥0)

Distinct1404
Distinct (%)0.3%
Missing2657
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean49.47479932
Minimum41.3283758
Maximum52.4001813
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:57.885873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41.3283758
5-th percentile41.386136
Q148.8346811
median51.5013149
Q351.5166755
95-th percentile52.369391
Maximum52.4001813
Range11.0718055
Interquartile range (IQR)2.6819944

Descriptive statistics

Standard deviation3.484356032
Coefficient of variation (CV)0.07042688561
Kurtosis0.7584232953
Mean49.47479932
Median Absolute Deviation (MAD)0.0445327
Skewness-1.452080311
Sum24024319.38
Variance12.14073696
MonotocityNot monotonic
2020-12-09T14:55:58.139082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51.501909747891.0%
 
51.511099342560.9%
 
51.49904635780.7%
 
51.510841232120.7%
 
51.510994529580.6%
 
51.519568826280.5%
 
51.493508325650.5%
 
51.502434825510.5%
 
51.513555522880.5%
 
51.557696222270.5%
 
Other values (1394)45453593.1%
 
(Missing)26570.5%
 
ValueCountFrequency (%) 
41.32837585720.1%
 
41.3684375750.1%
 
41.3703041229< 0.1%
 
41.37130810820.2%
 
41.3725246120< 0.1%
 
ValueCountFrequency (%) 
52.40018133120.1%
 
52.39248984670.1%
 
52.3923684143< 0.1%
 
52.38728848560.2%
 
52.385649410710.2%
 

lng
Real number (ℝ)

Distinct1404
Distinct (%)0.3%
Missing2657
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.630325181
Minimum-0.3192925
Maximum16.4217627
Zeros0
Zeros (%)0.0%
Memory size3.7 MiB
2020-12-09T14:55:58.376928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.3192925
5-th percentile-0.1949706
Q1-0.1444623
median-0.0071375
Q32.3556545
95-th percentile16.3546297
Maximum16.4217627
Range16.7410552
Interquartile range (IQR)2.5001168

Descriptive statistics

Standard deviation4.43069956
Coefficient of variation (CV)1.684468366
Kurtosis3.513074748
Mean2.630325181
Median Absolute Deviation (MAD)0.2217223
Skewness2.056953327
Sum1277251.714
Variance19.6310986
MonotocityNot monotonic
2020-12-09T14:55:58.609082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.023220847891.0%
 
-0.120867342560.9%
 
-0.191707335780.7%
 
-0.078058132120.7%
 
-0.186341729580.6%
 
-0.17052126280.5%
 
-0.183434625650.5%
 
-0.000249725510.5%
 
-0.18000222880.5%
 
-0.283526322270.5%
 
Other values (1394)45453593.1%
 
(Missing)26570.5%
 
ValueCountFrequency (%) 
-0.31929253910.1%
 
-0.306071128< 0.1%
 
-0.29150523850.1%
 
-0.2907066800.1%
 
-0.286494512120.2%
 
ValueCountFrequency (%) 
16.42176274260.1%
 
16.42009574310.1%
 
16.4133973191< 0.1%
 
16.41294935010.1%
 
16.411699792< 0.1%
 

Interactions

2020-12-09T14:55:23.100115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:23.308964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:23.517226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:23.736262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:23.964094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:24.190372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:24.410976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:24.616999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:24.821798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:25.028697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:25.231558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:25.453906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:25.699228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:25.924928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:26.142636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:26.365412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:26.572530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:26.920914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:27.126373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:27.343532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:27.585862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:27.831402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:28.097261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:28.337137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:28.572648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:28.806154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:29.030962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:29.245108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:29.446527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:29.661282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:29.873045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:30.091001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:30.307405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:30.528504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:30.731187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:30.936347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:31.145966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:31.364007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:31.610661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:31.841817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:32.071294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:32.304862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:32.550824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:32.771449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:32.987163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:33.216397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:33.429788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:33.659203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:33.886698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:34.263394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:34.494525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:34.726252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:34.947917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:35.167223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:35.381259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:35.625771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:35.856791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:36.082852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:36.299956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:36.523665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:36.749732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:36.967266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:37.178097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:37.387522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:37.602748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:37.811312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:38.066407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:38.284537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:38.502077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:38.725922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:38.953649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:39.155443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:39.353713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:39.559353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:39.773986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:39.997267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:40.214530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:40.433689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:40.658060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:40.867628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:41.068028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-09T14:55:58.868042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-09T14:55:59.088709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-09T14:55:59.325460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-09T14:55:59.549317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-09T14:55:42.361497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:43.627896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:45.413821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-09T14:55:46.097973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlng
0s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1948/3/20177.7Hotel ArenaRussiaI am so angry that i made this post available via all possible sites i use when planing my trips so no one will make the mistake of booking this place I made my booking via booking com We stayed for 6 nights in this hotel from 11 to 17 July Upon arrival we were placed in a small room on the 2nd floor of the hotel It turned out that this was not the room we booked I had specially reserved the 2 level duplex room so that we would have a big windows and high ceilings The room itself was ok if you don t mind the broken window that can not be closed hello rain and a mini fridge that contained some sort of a bio weapon at least i guessed so by the smell of it I intimately asked to change the room and after explaining 2 times that i booked a duplex btw it costs the same as a simple double but got way more volume due to the high ceiling was offered a room but only the next day SO i had to check out the next day before 11 o clock in order to get the room i waned to Not the best way to begin your holiday So we had to wait till 13 00 in order to check in my new room what a wonderful waist of my time The room 023 i got was just as i wanted to peaceful internal garden view big window We were tired from waiting the room so we placed our belongings and rushed to the city In the evening it turned out that there was a constant noise in the room i guess it was made by vibrating vent tubes or something it was constant and annoying as hell AND it did not stop even at 2 am making it hard to fall asleep for me and my wife I have an audio recording that i can not attach here but if you want i can send it via e mail The next day the technician came but was not able to determine the cause of the disturbing sound so i was offered to change the room once again the hotel was fully booked and they had only 1 room left the one that was smaller but seems newer3971403Only the park outside of the hotel was beautiful11.07.02.9[' Leisure trip ', ' Couple ', ' Duplex Double Room ', ' Stayed 6 nights ']0 days52.3605764.915968
1s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1948/3/20177.7Hotel ArenaIrelandNo Negative01403No real complaints the hotel was great great location surroundings rooms amenities and service Two recommendations however firstly the staff upon check in are very confusing regarding deposit payments and the staff offer you upon checkout to refund your original payment and you can make a new one Bit confusing Secondly the on site restaurant is a bit lacking very well thought out and excellent quality food for anyone of a vegetarian or vegan background but even a wrap or toasted sandwich option would be great Aside from those minor minor things fantastic spot and will be back when i return to Amsterdam105.07.07.5[' Leisure trip ', ' Couple ', ' Duplex Double Room ', ' Stayed 4 nights ']0 days52.3605764.915968
2s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/31/20177.7Hotel ArenaAustraliaRooms are nice but for elderly a bit difficult as most rooms are two story with narrow steps So ask for single level Inside the rooms are very very basic just tea coffee and boiler and no bar empty fridge421403Location was good and staff were ok It is cute hotel the breakfast range is nice Will go back21.09.07.1[' Leisure trip ', ' Family with young children ', ' Duplex Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']3 days52.3605764.915968
3s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/31/20177.7Hotel ArenaUnited KingdomMy room was dirty and I was afraid to walk barefoot on the floor which looked as if it was not cleaned in weeks White furniture which looked nice in pictures was dirty too and the door looked like it was attacked by an angry dog My shower drain was clogged and the staff did not respond to my request to clean it On a day with heavy rainfall a pretty common occurrence in Amsterdam the roof in my room was leaking luckily not on the bed you could also see signs of earlier water damage I also saw insects running on the floor Overall the second floor of the property looked dirty and badly kept On top of all of this a repairman who came to fix something in a room next door at midnight was very noisy as were many of the guests I understand the challenges of running a hotel in an old building but this negligence is inconsistent with prices demanded by the hotel On the last night after I complained about water damage the night shift manager offered to move me to a different room but that offer came pretty late around midnight when I was already in bed and ready to sleep2101403Great location in nice surroundings the bar and restaurant are nice and have a lovely outdoor area The building also has quite some character26.01.03.8[' Leisure trip ', ' Solo traveler ', ' Duplex Double Room ', ' Stayed 3 nights ']3 days52.3605764.915968
4s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/24/20177.7Hotel ArenaNew ZealandYou When I booked with your company on line you showed me pictures of a room I thought I was getting and paying for and then when we arrived that s room was booked and the staff told me we could only book the villa suite theough them directly Which was completely false advertising After being there we realised that you have grouped lots of rooms on the photos together leaving me the consumer confused and extreamly disgruntled especially as its my my wife s 40th birthday present Please make your website more clear through pricing and photos as again I didn t really know what I was paying for and how much it had wnded up being Your photos told me I was getting something I wasn t Not happy and won t be using you again1401403Amazing location and building Romantic setting8.03.06.7[' Leisure trip ', ' Couple ', ' Suite ', ' Stayed 2 nights ', ' Submitted from a mobile device ']10 days52.3605764.915968
5s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/24/20177.7Hotel ArenaPolandBackyard of the hotel is total mess shouldn t happen in hotel with 4 stars171403Good restaurant with modern design great chill out place Great park nearby the hotel and awesome main stairs20.01.06.7[' Leisure trip ', ' Group ', ' Duplex Double Room ', ' Stayed 1 night ']10 days52.3605764.915968
6s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/17/20177.7Hotel ArenaUnited KingdomCleaner did not change our sheet and duvet everyday but just made bed They also didn t clean the floor and changed the body gel when we run out of it331403The room is spacious and bright The hotel is located in a quiet and beautiful park18.06.04.6[' Leisure trip ', ' Group ', ' Duplex Twin Room ', ' Stayed 5 nights ', ' Submitted from a mobile device ']17 days52.3605764.915968
7s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/17/20177.7Hotel ArenaUnited KingdomApart from the price for the brekfast Everything very good111403Good location Set in a lovely park friendly staff Food high quality We Oth enjoyed the breakfast19.01.010.0[' Leisure trip ', ' Couple ', ' Duplex Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']17 days52.3605764.915968
8s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/9/20177.7Hotel ArenaBelgiumEven though the pictures show very clean rooms the actual room was quit dirty and outlived Also check in is at 15 o clock but our room was not ready at that time341403No Positive0.03.06.5[' Leisure trip ', ' Couple ', ' Duplex Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']25 days52.3605764.915968
9s Gravesandestraat 55 Oost 1092 AA Amsterdam Netherlands1947/8/20177.7Hotel ArenaNorwayThe aircondition makes so much noise and its hard to sleep at night151403The room was big enough and the bed is good The breakfast food and service on the hotel is good outside the hotel there is a big park which is very good for walk in the morning and evening Many people are having picnics and do some bicycling50.01.07.9[' Leisure trip ', ' Couple ', ' Large King Room ', ' Stayed 7 nights ']26 days52.3605764.915968

Last rows

Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlng
488234Via Senato 22 Milan City Center 20121 Milan Italy11611/22/20159.0Senato Hotel MilanoBulgariaNon21031Very modern rooms comfortable bed Perfect breacfast great location11.011.09.6[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']620 day45.4714069.19653
488235Via Senato 22 Milan City Center 20121 Milan Italy11611/21/20159.0Senato Hotel MilanoUnited States of AmericaNo Negative01031Very helpful people A lovely design of the rooms and public spaces14.01.08.8[' Business trip ', ' Group ', ' Double Room ', ' Stayed 3 nights ', ' Submitted from a mobile device ']621 day45.4714069.19653
488236Via Senato 22 Milan City Center 20121 Milan Italy11611/19/20159.0Senato Hotel MilanoUnited States of AmericaNo Negative01031a little far from subway but very close to the main fashion area15.07.09.2[' Leisure trip ', ' Couple ', ' Double Room with Terrace ', ' Stayed 1 night ']623 day45.4714069.19653
488237Via Senato 22 Milan City Center 20121 Milan Italy11611/17/20159.0Senato Hotel MilanoHong KongNo Negative01031Easy to access all the major area Staffs are nice Room r tidy with all the essential19.01.08.3[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 4 nights ', ' Submitted from a mobile device ']625 day45.4714069.19653
488238Via Senato 22 Milan City Center 20121 Milan Italy11611/16/20159.0Senato Hotel MilanoBelgiumNo Negative01031Beautiful design perfect location great breakfast And friendly staf11.029.010.0[' Leisure trip ', ' Family with older children ', ' Superior Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']626 day45.4714069.19653
488239Via Senato 22 Milan City Center 20121 Milan Italy11611/12/20159.0Senato Hotel MilanoSaudi ArabiaThe room was very small71031The location and cleanless5.09.06.3[' Leisure trip ', ' Solo traveler ', ' Single Room ', ' Stayed 1 night ']630 day45.4714069.19653
488240Via Senato 22 Milan City Center 20121 Milan Italy11611/12/20159.0Senato Hotel MilanoUnited States of AmericaThe pendant lights at the night stand only worked one side Far walk from subway171031Nicely designed hotel Good location near the action10.07.08.8[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']630 day45.4714069.19653
488241Via Senato 22 Milan City Center 20121 Milan Italy11611/10/20159.0Senato Hotel MilanoTurkeyNo Negative01031Rooms Breakfast Location4.03.010.0[' Leisure trip ', ' Couple ', ' Double Room ', ' Stayed 3 nights ']632 day45.4714069.19653
488242Via Senato 22 Milan City Center 20121 Milan Italy11611/8/20159.0Senato Hotel MilanoIndiaNo Negative01031Excellent ambience created by very thoughtful interior design10.04.09.2[' Leisure trip ', ' Solo traveler ', ' Classic Double Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']634 day45.4714069.19653
488243Via Senato 22 Milan City Center 20121 Milan Italy11610/20/20159.0Senato Hotel MilanoEgyptNo Negative01031Room and bathroom were really comfortable and verNaNNaNNaNNaNNaNNaNNaN

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Hotel_AddressAdditional_Number_of_ScoringReview_DateAverage_ScoreHotel_NameReviewer_NationalityNegative_ReviewReview_Total_Negative_Word_CountsTotal_Number_of_ReviewsPositive_ReviewReview_Total_Positive_Word_CountsTotal_Number_of_Reviews_Reviewer_Has_GivenReviewer_ScoreTagsdays_since_reviewlatlngcount
0100 110 Euston Road Camden London NW1 2AJ United Kingdom7282/25/20178.9Pullman London St PancrasChinanone23168good location and super nice staff room is big and great13.039.09.6[' Business trip ', ' Solo traveler ', ' Classic King Room ', ' Stayed 1 night ', ' Submitted from a mobile device ']159 day51.528677-0.1283492
116 22 Great Russell Street Camden London WC1B 3NN United Kingdom3007/27/20179.0The Bloomsbury HotelIsraelNo Negative01254The attention received by Sebastian and his team was exceptional12.04.09.6[' Leisure trip ', ' Couple ', ' Superior Double Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']7 days51.517167-0.1290532
2167 rue de Rome 17th arr 75017 Paris France1110/14/20166.8Villa EugenieAlbaniaThey didnt change the sheets They also had a minibar coca and nestea cannets They didnt tell us that it was with payment and we had to pay 30 euros for 5 of them What an abusive bill40165The room was cozy and pretty7.03.05.8[' Leisure trip ', ' Couple ', ' Standard Double or Twin Room ', ' Stayed 3 nights ']293 day48.8871282.3142052
3167 rue de Rome 17th arr 75017 Paris France1110/14/20166.8Villa EugenieIranEvry thing was wrong Cold room Dark room No refrigetor in room No ac Evry thing was baaaaaaad Very bad21165This hotel was terrible this place worst place in paris Hostel is better than this place I m sorry for myself to spend my time in this place29.02.02.5[' Solo traveler ', ' Single Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']293 day48.8871282.3142052
4167 rue de Rome 17th arr 75017 Paris France1110/14/20166.8Villa EugenieUnited KingdomEvry thing of this place i can t name horlrel to this place was wrong and out of repair all lamp were nt work 2nights i got cold in cold room Ac not working i so sorry for my self that i had to spend my time in this ruin place 4star hotel has nt refrigerator58165This hotel is worst hotel Its terrible9.01.02.5[' Solo traveler ', ' Single Room ', ' Stayed 2 nights ', ' Submitted from a mobile device ']293 day48.8871282.3142052
5167 rue de Rome 17th arr 75017 Paris France1110/18/20166.8Villa EugenieIsraelNo Negative0165the room was very french and beautiful it was good location and I enjoyed to stay there18.01.09.2[' Leisure trip ', ' Couple ', ' Twin Room ', ' Stayed 3 nights ']289 day48.8871282.3142052
6167 rue de Rome 17th arr 75017 Paris France1110/18/20166.8Villa EugenieUnited KingdomNo internet it was important for me for my business Really Small Room Very Bad Location No Shampoo and staff etc You need to call them to bring All Bad sorry32165Didier was friendly and good7.012.02.5[' Leisure trip ', ' Couple ', ' Standard Double or Twin Room ', ' Stayed 4 nights ']289 day48.8871282.3142052
7167 rue de Rome 17th arr 75017 Paris France1110/19/20166.8Villa EugenieSwitzerlandWifi was not working6165No Positive0.01.07.9[' Leisure trip ', ' Couple ', ' Standard Double or Twin Room ', ' Stayed 2 nights ']288 day48.8871282.3142052
8167 rue de Rome 17th arr 75017 Paris France1110/2/20166.8Villa EugenieQatarStaff very rude My credit card was charged Before my stay and while Checking out they charged me again when I told the receptionist about it her answer was I m not trying to steal your money madam in a ver un polite way Blaming my Bank about it Very poor selection for breakfast56165Nothing3.08.05.0[' Leisure trip ', ' Family with young children ', ' Two Connecting Double Rooms ', ' Stayed 2 nights ', ' Submitted from a mobile device ']305 day48.8871282.3142052
9167 rue de Rome 17th arr 75017 Paris France1110/20/20166.8Villa EugenieLithuaniaWe booked hotel for 3 nights and there was no normal internet connection From all trip time we had internet only for 1 2 huors Also there is very noisy almoust impossible to sleep with open window39165Good place4.09.06.7[' Business trip ', ' Group ', ' Superior Double Room ', ' Stayed 3 nights ']287 day48.8871282.3142052